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Paper Abstract and Keywords
Presentation 2018-08-20 11:20
A Study of Autoregressive Model and Autoregressive Integrated Model Based Channel Idle/ Busy Status Duration Prediction for Real Environment WLAN Channel
Naoya Hokimoto, Yafei Hou, Satoshi Denno (Okayama Univ.) SRW2018-13
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, due to the increase of huge number of wireless devices such as smartphones or sensors, mobile wireless traffic is dramatically expanding each year. Therefore, how to improve spectrum efficiency (SE) for cognitive wireless system is important and urgent research topic. Till now, there are many researches considering the prediction of Channel Occupancy Ratio (COR: the ration between busy duration length to resolution period $T$). If the start and end points of Busy and Idle duration can be correctly predicted, it will largely benefit the wireless system design and SE improvement. In this paper, we will consider such research based on autoregressive (AR) and autoregressive integrated (ARI) models using traffic data captured from the wireless channel in real environment. The major idea is that the Busy and Idle duration length can be calculated from COR value when the resolution period $T$ is short. In this paper, we first investigate the COR prediction performance using AR and ARI predictors with different value of $T$. Then using relationship between the Busy and Idle duration length and COR value, the Busy and Idle duration length prediction can be realized. From the results, we can confirm our proposal has better prediction accuracy than that of AR/ARI predictor using only Busy and Idle duration traffic data.
Keyword (in Japanese) (See Japanese page) 
(in English) Autoregressive model / Autoregressive Integrated model / Channel Occupancy Ratio prediction / prediction of Idle/Busy duration / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 183, SRW2018-13, pp. 25-30, Aug. 2018.
Paper # SRW2018-13 
Date of Issue 2018-08-13 (SRW) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SRW  
Conference Date 2018-08-20 - 2018-08-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Okayama Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network, MAC, Sensor technology, mmWave, etc. 
Paper Information
Registration To SRW 
Conference Code 2018-08-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Autoregressive Model and Autoregressive Integrated Model Based Channel Idle/ Busy Status Duration Prediction for Real Environment WLAN Channel 
Sub Title (in English)  
Keyword(1) Autoregressive model  
Keyword(2) Autoregressive Integrated model  
Keyword(3) Channel Occupancy Ratio prediction  
Keyword(4) prediction of Idle/Busy duration  
1st Author's Name Naoya Hokimoto  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Yafei Hou  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Satoshi Denno  
3rd Author's Affiliation Okayama University (Okayama Univ.)
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Date Time 2018-08-20 11:20:00 
Presentation Time 25 
Registration for SRW 
Paper # IEICE-SRW2018-13 
Volume (vol) IEICE-118 
Number (no) no.183 
Page pp.25-30 
#Pages IEICE-6 
Date of Issue IEICE-SRW-2018-08-13 

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